For mass application positioning demands, the current single positioning sensor cannot provide reliable and accurate positioning. Herein, we present batch inverse covariance intersection (BICI) and BICI with interacting multiple model (BICI-IMM) multi-sensor fusion positioning methods, which are based on the batch form of the sequential inverse covariance intersection (SICI) fusion method. Meanwhile, it is proved that the BICI is robust. Compared with SICI, BICI-IMM reduces estimation error variance of the motion model and has less conservativeness. The BICI-IMM algorithm improves the accuracy of local filtering by interacting with multiple models and realizes global fusion estimation based on BICI. The validity of the BICI and BICI-IMM algorithm are demonstrated by two simulations and experiments in the open and semi-open scenes, and its positioning accuracy relations are shown. In addition, it is demonstrated that the BICI-IMM algorithm can improve the positioning accuracy in the actual scenes.

The Global Navigation Satellite System (GNSS) has been widely used in every area of our daily life to provide accurate Positioning, Navigation, and Timing (PNT) services. However, due to the multipatch effect and an obstructed view of the satellite, GNSS receivers are susceptible to large-ranging errors, which are particularly prevalent in urban areas where precise positioning is indispensable. The deployment of the high-spatial-density Fifth-Generation (5G) network makes it possible to provide a broad area with high-precision positioning service. Obviously, it promoting the deep integration of the GNSS system and the 5G mobile communication network and establishing a Highly Dependable Spatio-temporal Network (HDSN) have become an inevitable trend. The existing algorithm for the fusion of multiple signals has difficulty settling problems such as the fast fluctuation of available signal sources and the poor stability of multi-scale multi-type signal estimation in GNSS-5G hybrid networks. Here, we propose a Square Root Unscented Stable Filter (SRUSF) for GNSS and 5G joint positioning with a compact coupled filter group architecture in a highly dependable spatio-temporal network. A stabilized coefficient is added to guarantee positive covariance of the estimation error. The possibility of divergence of filtering results due to the variation in signal sources and the incomplete agreement between the system model and the actual situation are reduced. The simulation results show that the proposed SRUSF method substantially enhances the positioning accuracy and reliability compared with the other five joint estimation methods for multiple signals. This work will enable the terminal of mass users to provide timing and positioning services with unprecedented accuracy and dependability under the GNSS and 5 G-based spatio-temporal network’s architecture.

In practical array signal processing applications, direction-of-arrival (DOA) estimation often suffers from degraded accuracy under low signal-to-noise ratio (SNR) and limited snapshot conditions. To address these challenges, we propose an off-grid DOA estimation method based on Fast Variational Bayesian Inference (OGFVBI). Within the variational Bayesian framework, we design a fixed-point criterion rooted in root-finding theory to accelerate the convergence of hyperparameter learning. We further introduce a grid fission and adaptive refinement strategy to dynamically adjust the sparse representation, effectively alleviating grid mismatch issues in traditional off-grid approaches. To address frequency dispersion in wideband signals, we develop an improved subspace focusing technique that transforms multi-frequency data into an equivalent narrowband model, enhancing compatibility with subspace DOA estimators. We demonstrate through simulations that OGFVBI achieves high estimation accuracy and resolution while significantly reducing computational time. Specifically, our method achieves more than 37.6% reduction in RMSE and at least 28.5% runtime improvement compared to other methods under low SNR and limited snapshot scenarios, indicating strong potential for real-time and resource-constrained applications.

Unmanned aerial vehicles (UAVs) are increasingly used indoors for inspection, security, and emergency tasks. Achieving accurate and robust localization under Global Navigation Satellite System (GNSS) unavailability and obstacle occlusions is therefore a critical challenge. Due to their inherent physical limitations, Inertial Measurement Unit (IMU)–based localization errors accumulate over time, Ultra-Wideband (UWB) measurements suffer from systematic biases in Non-Line-of-Sight (NLOS) environments and Visual–Inertial Odometry (VIO) depends heavily on environmental features, making it susceptible to long-term drift. We propose a tightly coupled fusion framework based on the Error-State Kalman Filter (ESKF). Using an IMU motion model for prediction, the method incorporates raw UWB ranges, VIO relative poses, and TFmini altitude in the update step. To suppress abnormal UWB measurements, a multi-epoch outlier rejection method constrained by VIO is developed, which can robustly eliminate NLOS range measurements and effectively mitigate the influence of outliers on observation updates. This framework improves both observation quality and fusion stability. We validate the proposed method on a real-world platform in an underground parking garage. Experimental results demonstrate that, in complex indoor environments, the proposed approach exhibits significant advantages over existing algorithms, achieving higher localization accuracy and robustness while effectively suppressing UWB NLOS errors as well as IMU and VIO drift.

In recent years, with the vigorous construction of 5G networks, the high-density deployment, low delay, and high bandwidth of 5G network systems have enabled high-precision positioning services. By integrating the BeiDou Navigation Satellite System (BDS) and 5G systems, more reliable positioning services can be provided, and BDS–5G signal integrated positioning has become a new research hotspot. However, BDS–5G signal fusion positioning faces the problems of how to build an effective fusion positioning model between heterogeneous systems and the high complexity of multiobjective function positioning solutions. Therefore, this paper constructs a TOA/TDOA fusion positioning model of BDS–5G signals and introduces the multiobjective particle swarm optimization positioning solution method (MOPSO) to realize the reliable positioning of BDS and 5G signal system fusion (MOPSO-TOA/TDOA). The experimental results show that compared with the traditional BDS–5G signal fusion positioning algorithm based on a Kalman filter, the positioning accuracy of the BDS–5G signal joint solution system based on a multiobjective particle fusion algorithm is 24.8% higher than that of the Kalman filter in terms of positioning convergence time, 18.9% higher in terms of system positioning accuracy, and 50–80% higher than that of the BDS positioning system alone, and the reliable positioning ability in complex environments is effectively enhanced.

In urban and indoor settings, the efficacy of the global navigation satellite system is notably limited, prompting a shift toward utilizing cellular and wireless signals for location services. However, existing methods struggle to discern short-delay multipath signals in intricate indoor environments, often faltering in the presence of non-Gaussian noise. This article introduces the Recursive Maximum Correntropy Criterion based Short-Delay Multipath Estimation (RMCSME) algorithm as a solution. By leveraging a short-delay multipath signal processing model and the recursive correntropy criterion, RMCSME accurately estimates dynamic multipath signals in the presense of non-Gaussian noise challenges. Through simulations and empirical signal tests, RMCSME demonstrates a marked reduction in ranging errors attributable to multipath effects while maintaining computational efficiency. Comparative analyzes with the Improved Multipath Estimation Delay-Locked Loop (IMEDLL) and Multiple Signal Classification (MUSIC) algorithms reveal that the RMCSME algorithm performs better in static experiments. In dynamic tests, RMCSME achieves a positioning accuracy of 0.29 m, surpassing IMEDLL by 21.7% and MUSIC by 39.6%. Furthermore, this approach presents a novel strategy for mitigating short-delay multipath errors in indoor 5G positioning signals, providing crucial support for achieving precise localization in commercial 5G networks.